autoskill

autoskill

Requires a running screenpipe daemon. This skill has no alternate data source — it reads exclusively from the local screenpipe HTTP API (default http://localhost:3030). If the daemon isn't running, run() raises ScreenpipeUnreachable with install instructions.

Network access & environment variables. This skill makes authenticated HTTP requests to (a) the user's local screenpipe daemon on loopback, and (b) the user-configured LLM backend — one of http://localhost:1234/v1 (LM Studio, default), https://api.anthropic.com (opt-in Claude), or a user-supplied BYOK Foundry gateway. The skill reads three environment variables — SCREENPIPE_TOKEN, ANTHROPIC_API_KEY, FOUNDRY_API_KEY — and uses each only to authenticate to the single endpoint its name implies. No other network destinations, no telemetry, no data egress to any third party.

Overview

Turn the user's own workflow history — captured passively by the local screenpipe daemon — into new skills. This skill is on-demand: the user invokes it with a time window, it queries screenpipe's local HTTP API, clusters repeated workflow patterns, compares each pattern against the existing skills in this repo, and produces a staged folder of proposals the user can review, edit, and promote.

When to Use This Skill

Invoke this skill when the user asks to:

• "Analyze my last 4 hours / day / week and propose new skills."
• "Look at what I've been doing and tell me what's not covered yet."
• "Draft a skill from my recent workflow."
• "Find composition recipes for workflows I repeat."

Do not invoke it for one-off questions about screenpipe itself, for real-time screen queries, or without an explicit user request — the skill analyzes sensitive local content and must stay explicitly user-triggered.

Privacy Posture

• Screenpipe handles app/window filtering at capture time. Install a starter deny-list by copying references/screenpipe-config.yaml into the user's screenpipe config. Sensitive apps (password managers, messaging, banking) are never OCR'd in the first place.
• Raw OCR never leaves the machine. scripts/fetch_window.py pulls data over localhost HTTP. scripts/cluster.py reduces the timeline to app/duration/title summaries. scripts/redact.py strips emails, API keys, bearer tokens, and phone numbers as defense-in-depth before any cluster summary reaches the LLM.
• LLM backend defaults to local. The recommended setup is LM Studio running Gemma-4-31B-it — strong reasoning at a size that fits on most workstation GPUs, and no data ever leaves your machine. Cloud backends (claude, foundry) are opt-in and documented in config.yaml for users who explicitly want them. Detection and embeddings always run locally regardless of backend choice.
• Dry-run mode (--plan) prints the exact timeline that will be analyzed before any LLM call.
• TLS for localhost (optional, for corporate policy): see references/https-proxy.md for the Caddy pattern.